R v/s Python has been an ongoing debate for quite a long time now. There is huge support for both R as well as Python. Both are fantastic languages that have immense community support and are great to learn and work with. The majority of industry experts have concluded that there is no single winner in Python v/s R, rather the best way is to figure out the best way to utilize both languages effectively.
Let us discover the benefits and drawbacks of both these languages and determine which will be the right fit for you.
It is the most popular Object-Oriented Programming Language used for general purposes. Python is famous for its ease to learn and use thanks to its easy readability feature with lots of white space and indentations. Python came into existence in 1989 and quickly became the favorite language of developers alongside C and Java.
Python is extensively used for Data Science purposes. The advantage of using Python is that there are lots of libraries that you can use to funnel your tasks.
Numpy, Pandas, Matplotlib, etc., are used for a variety of tasks for creating Data Visualizations, etc.
Due to its high popularity, Python is increasingly being used in the domain of Machine Learning, Deep Learning, and Artificial Intelligence. It has various tools and libraries specifically for these domains like sci-kit-learn, Keras, TensorFlow, etc. Many developing environments are used to write and execute Python codes. There are open source web applications like Jupyter Notebook used for sharing documents containing Python code, visualizations, equations, and other explanations for Data Science. Check out Python Certification Courses.
R is also a popular programming language that is as famous as Python, and both are open-source too. R has a lively ecosystem, supports complex Data Modelling, and has a variety of different tools for reporting data. At the time of writing this article, there are approximately 17948 packages shared via CRAN (Comprehensive R Archive Network) repository.
R is very famous in the Data Science domain and used by professionals and researchers in the Data Science domain. R offers various tools and libraries for various processes like:
Data Cleansing and Data Preparation, building visualizations, training and evaluating ML models, for Deep Learning algorithms, etc.
Let’s talk about Data Science applications alone to make the point clearer. Although both languages are used for Data Science applications and have an equally strong community that extends support for an extensive range of tools and libraries. R is primarily used for statistical analysis, whereas Python is used for general Data Science applications like Data Wrangling and many more.
Python is a general-purpose programming language that emphasizes more on code readability that makes it easy to digest and use. Developers use Python more for Data Analysis or building scalable Machine Learning models.
Whereas statisticians built R for statisticians! R is solely used in the Data Science domain and is not a multi-purpose language like Python. R is used heavily for working with statistical models, specialized analytics, etc. R uses fewer lines of code but has beautiful Data Visualization as its strength.
Data Collection: Python language does support data formats ranging from CSV format to JSON. In R though, it only allows its users to import data ranging from Excel, text, and CSV files.
Data Exploration: You can do Data Exploration with Python in really no time using the Pandas library. R offers you many options for Data exploration.
Data Modelling: For Data Modelling, Python has libraries like Numpy for Numerical Analysis, Scikit-learn for dealing with Machine Learning algorithms, and SciPy is for scientific computing. Whereas in R, you have to use external packages most of the time, but there is a library named Tidyverse that makes the job of importing, manipulating, visualizing, and reporting data easy.
Data Visualization: Python’s weak point in Visualization is very far from the exceptional standards set by R. The Matplotlib library helps you in dealing with charts and graphs. You can also use the Seaborn library. R is from the ground-up made to offer excellent visualizations.
Both languages have their pros and cons. You must ask several questions to make your choice clear. Python is easy and simple to learn and use. The learning curve of Python is linear. R is also easy to learn and use, but advanced functionalities are a little tough to master. If you wish to master R, check out the R Programming tutorial.
Which language your company uses is also another question that you should know the answer to. If they are preferring to use Python, then learn Python and if they resort to R, then it’s R. R is solely used in the Data Science domain, whereas Python is a general-purpose language.
The problems you are trying to solve play another major role in deciding the language you will choose. R is better suited for statistics, having many libraries for Data Exploration, etc. Choose Python if you are more focused on Machine Learning and performing Data Analysis. Choose R if you want better visualization and graphics.